Opportunities in Georgia

Main Responsibilities

• Design and build agentic AI systems, including autonomous agents, multi-agent orchestration, workflow state machines, and tool-using agents.

• Develop LLM-driven agents capable of reasoning, planning, retrieval (RAG), and task execution across enterprise systems.

• Build and maintain AI-powered automation workflows using platforms like n8n and Make to orchestrate business processes and cross-application integrations.

• Integrate agents with APIs, CRM/ERP systems, collaboration tools, databases, and payment platforms using tool/function calling, MCP, and A2A patterns.

• Implement robust execution logic (validation, retries, rate limits, fallbacks, error handling) to ensure reliability and scalability.

• Design and manage RAG pipelines using embeddings, vector databases, chunking, and reranking strategies.

• Establish safety guardrails, access controls, and human-in-the-loop workflows for high-risk actions.

• Build evaluation, observability, and tracing pipelines to monitor performance, cost, latency, and reliability.

• Deploy and operate agent services in cloud environments (AWS, Azure, or GCP) using Docker, Kubernetes, Terraform, and CI/CD.

• Monitor production systems, troubleshoot issues, and continuously improve agent performance and policies.

• Prototype and benchmark emerging agentic AI frameworks and models.

• Create technical documentation and communicate AI solutions effectively to cross-functional stakeholders.

Qualifications And Requirements

• Bachelor’s or Master’s degree in Computer Science, AI, Engineering, or related field.
• 3+ years of software engineering experience (Python and/or TypeScript).
• 1+ year building LLM-powered or agentic AI systems in production or near-production environments.
• Experience with agent frameworks (e.g., LangChain, LangGraph, AutoGen, CrewAI, Semantic Kernel).
• Hands-on experience with automation/orchestration tools (e.g., n8n, Make) in production settings.
• Strong understanding of LLMs, embeddings, prompt engineering, structured outputs, and tool calling.
• Experience designing REST APIs, microservices, and backend systems.
• Familiarity with vector databases and RAG architectures.
• Strong system design, debugging, and communication skills.

Preferred

• Experience with MCP, A2A, or advanced agent communication patterns.
• Advanced experience with n8n (custom nodes, self-hosting) or Make (complex scenarios).
• Experience combining LLMs with workflow engines for document processing, reporting, chatbots, or decision support.
• Familiarity with AI evaluation and observability tools (e.g., LangSmith, OpenAI Evals, Weights & Biases).
• Experience with multi-agent systems, planning algorithms, RL, fine-tuning, or RLHF.
• Knowledge of CI/CD pipelines and security best practices.
• Experience in regulated industries (e.g., healthcare, finance, defense).
• Relevant cloud or ML certifications.
• Experience with cloud platforms (AWS, Azure, or GCP), containerization (Docker, Kubernetes), and infrastructure-as-code tools.

• Bachelor’s or Master’s degree in Computer Science, AI, Engineering, or related field.
• 3+ years of software engineering experience (Python and/or TypeScript).
• 1+ year building LLM-powered or agentic AI systems in production or near-production environments.
• Experience with agent frameworks (e.g., LangChain, LangGraph, AutoGen, CrewAI, Semantic Kernel).
• Hands-on experience with automation/orchestration tools (e.g., n8n, Make) in production settings.
• Strong understanding of LLMs, embeddings, prompt engineering, structured outputs, and tool calling.
• Experience designing REST APIs, microservices, and backend systems.
• Familiarity with vector databases and RAG architectures.
• Strong system design, debugging, and communication skills.

Preferred

• Experience with MCP, A2A, or advanced agent communication patterns.
• Advanced experience with n8n (custom nodes, self-hosting) or Make (complex scenarios).
• Experience combining LLMs with workflow engines for document processing, reporting, chatbots, or decision support.
• Familiarity with AI evaluation and observability tools (e.g., LangSmith, OpenAI Evals, Weights & Biases).
• Experience with multi-agent systems, planning algorithms, RL, fine-tuning, or RLHF.
• Knowledge of CI/CD pipelines and security best practices.
• Experience in regulated industries (e.g., healthcare, finance, defense).
• Relevant cloud or ML certifications.
• Experience with cloud platforms (AWS, Azure, or GCP), containerization (Docker, Kubernetes), and infrastructure-as-code tools.